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What is ocr.png

OCR stands for Optical Character Recognition. It allows text from paper documents to be digitized to be searched or edited by other software applications. OCR converts typed or printed text from digital images of physical documents into machine readable, encoded text

This conversion allows Grooper to search text characters from the image, providing the capability to separate images into documents, classify them and extract data from them. The Grooper activity that performs OCR is Recognize. Including a Recognize step in your batch process will allow you to OCR image-based content.


The quick explanation of OCR is it analyzes pixels on an image and translates those pixels into text. Most importantly, it translates pixels into machine readable text. Grooper can be described as a document modeling platform. You use the platform to model how pages are separated out into documents, how one document gets put into one category or another, and how extractable data is structured on the document. Once you have this model of what a document is, how it fits into a larger document set, and where the data is on it, you can use it to programmatically process any document that fits the model.

In order to do any of that, you have to be able to read the text on the page. How do you know an invoice is an invoice? A simple way could be locating the word "invoice" (or other text associated with the invoice). You, as a human, do this by looking at the ink on a page (or pixels for a digital document) and reading the word "invoice". Grooper does this by using a Data Extractor (and regular expression) to read the machine readable text for the page. OCR is how each page gets that machine readable text in order to model the document set and process it.

The General Process

In Grooper, OCR is performed by the Recognize activity, referencing an OCR Profile which contains all the settings to get the OCR results, including which OCR Engine is used. The OCR Profile also has settings to optionally process those results to increase the accuracy of the OCR Engine used. The general process of OCR'ing a document is as follows in Grooper:

1) The document image is handed to the Recognize activity, which references an OCR Profile, containing the settings to perform the OCR operation.

2) The OCR Engine (set on the OCR Profile) converts the pixels on the image into machine readable text for the full page.

3) Grooper reprocesses the OCR Engine's results and runs additional OCR passes using the OCR Profile's Synthesis properties.

4) The raw OCR results from the OCR Engine and Grooper's Synthesis results are combined into a single text flow.

5) Undesirable results can be filtered out using Grooper's Results Filtering options.

The Recognize activity is handed the document image and performs OCR.
Ocr results 1.png
The results are seen here in a text flow.
Ocr results 2.png
The results are seen here in a "Layout View", using the character positions and font sizes obtained to overlay where they are on the document.
Ocr results 3.png

OCR vs. Native Text

OCR gets text specifically from images, whether they were printed and scanned or imported from a digital source. However, what if the document created digitally and imported in its original digital form? Wouldn't it have been created on a computer, using machine readable text? Most likely, yes! If a form was created using a product like Adobe Acrobat and filled in using a computer, the text comprising the document and the filled fields is encoded within the document itself. This is called "Native Text". This text is already machine readable. So there is no reason to OCR the document. Instead, the native text is extracted via Grooper's native text extraction. Native text has a number of advantages over OCR. OCR is not perfect. As you will see, OCR is a fairly complicated process with a number of opportunities to misread a document. Grooper has plenty of advancements to get around these errors and produce a better result, but OCR will rarely be as accurate as the original native text from a digital document.

However, be careful. Just because a PDF document has machine readable text behind it, does not mean that text is native text. If the document was OCR'd by a different platform, the text may have been inserted into the PDF (Grooper also has this capability upon exporting document). In these cases, we still recommend OCR'ing the document to take advantage of Grooper's superior OCR capabilities and get a more accurate result.

Regardless whether getting machine readable text through OCR or Native Text Extraction, both are done via the Recognize activity. In the case of OCR, you will need to create an OCR Profile containing all the settings to perform OCR and reference it during the Recognize activity. Native Text Extraction is enabled by default, but can be disabled if you wish to use OCR instead.

What is an OCR Engine?

OCR Engines are software applications that perform the actual recognition of characters on images, analyzing the pixels on the image and figuring out what text characters they match.

OCR Engines themselves have three phases:

1) Pre-Processing: In this phase, the OCR image prepares the image to be read by turning color and grayscale images to black and white and potentially removing artifacts getting in the way of OCR, such as specks and lines. Text is also segmented from lines to words and finally characters in this phase as well.

2) Character Recognition: Here, the OCR Engine takes those pixel character segments, compares them to examples of character glyphs, and makes a decision about which machine readable text character matches the segment.

3) Post-Processing: Commercial OCR Engines also analyze the OCR results and attempt to correct inaccurate results, such as performing basic spellchecking.

For more in depth information on how OCR Engines work, visit the OCR Engine article.

The Transym 4 and Transym 5 OCR engines are included in Grooper's licensing. Transym OCR 4 provides highly accurate English-only OCR while Transym OCR 5 provides multi-language OCR for 28 different languages. Google's open source Tesseract engine is available in version 2.72 and beyond. ABBY FineReader, Prime OCR, and Azure OCR are also supported but require separate installations and separate licensing.

Image Processing and OCR

Regardless of how good an OCR Engine is, OCR is very rarely perfect. Characters can be segmented out from words wrong. Artifacts such as table lines, check boxes or even just specks from image noise can interfere with character segmenting and character recognition. Even when they are segmented out correctly, the OCR Engine's character recognition can make the wrong decision about what the character is.

Image Processing (often abbreviated as "IP") can assist the OCR operation by providing a "cleaner" image to the OCR Engine. The general idea is to give the OCR engine just the text pixels, so that is all the engine needs to process.

This image is much easier for OCR to process... ...than this image.
Ocr ip 1.png Ocr ip 2.png

Images are altered using an IP Profile, which contains a step by step list of IP Commands, each of which performs a specific alteration to the image. IP Profiles are highly configurable. There are multiple different IP Commands, each of which has its own configurable properties as well. In the example above, the image was altered using an IP Profile with six steps, each step containing a different IP Command.

However, for the example above, the IP Profile's result is drastically different from the original image. While it certainly helps the OCR result, it's likely, at the end of the process, you want to export a document that looks more like the "before" picture than the "after". Luckily, Image Processing can be performed in two ways:

  1. Permanent for archival purposes.
  2. Temporary for non-destructive OCR cleanup.

For more information on Image Processing, visit the Image Processing article.

OCR Synthesis

The Synthesis functionality is Grooper's unique method of pre-processing and re-processing raw results from the OCR engine to get better results out of it. Using Synthesis, portions of the document can be OCR'd independently from the full text OCR, portions of the image dropped out from the first OCR pass can be re-run, and certain results can be reprocessed. The results from the Synthesis operation then get combined with the full text OCR results from the OCR Engine into a single text flow.

Synthesis is a collection of five separate OCR processing operations:

  • Bound Region Processing
  • Iterative Processing
  • Cell Validation
  • Segment Reprocessing
  • Font Pitch Detection

As separate operations, the user can choose to enable all four operations, choose to use only one, or any combination. Synthesis is enabled on OCR Profiles, using the "Synthesis" property. This property is enabled by default on OCR Profiles (and can be disabled if you so choose). However, each Synthesis operation needs to be configured independently in order to function.

For more information on each operation, visit the Synthesis article.